White Collar Agent = a computer-use AI agent with TUI interface

Hacker News

Happy to announce that we have just launched White Collar Agent — an open-source general computer-use AI agent that helps you automate computer-based tasks with a TUI interface ( https://github.com/zfoong/WhiteCollarAgent ). The agent can autonomously interpret your instructions, plan actions, and execute tasks to achieve a wide range of automation goals. The code also serves as a foundation for building your own AI agents. It can perform web tasks and automate OS operations like high-volume repetitive work and batch processing. Want to translate a whole directory of documents into Japanese? Organize a messy folder of files based on what is inside each file? Scan a folder of images and auto-generate captions for each one? These are tasks that can be easily automated by White Collar Agent. P.S. You are free to use, host, and even monetize White Collar Agent. If you are an AI engineer or builder, there is a custom agent layer that lets you create a specialized agent you can host. All you have to do is inject the agent’s identity and custom tools. The GUI mode is still in the experimental phase. If you have any experience developing a GUI agent, we invite you to work together (please reach out to us!).

Visit product View on Hacker NewsDec 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, computer · Missing: mac, macos, cursor

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, interface, builder · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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